Dimensionality Reduction (DR) approaches for spectral HSI representation can either be supervised or unsupervised. Unsupervised techniques transform the high dimensional HSI into a low dimensional space without using the class label information, for example, Principal Component Analysis (PCA) and locally linear embedding [1]}. On the other hand, supervised DR methods utilize labeled samples to learn the data distribution i.e. to keep data points of the same classes near to each other and separate the data points of different classes. For instance, linear discriminant analysis (LDA), local Fisher discriminant analysis (LFDA) [2]}, local discriminant embedding (LDE) [3]} and nonparametric weighted feature extraction (NWFE) [4]}. LDA and LDFA provide better class separability by maximizing the inter-class distance of data points and minimizing the intra-class distance. However, due to the spectral mixing effect, in which the same material may appear with different spectra or different materials may have the same spectral signatures, it becomes difficult to differentiate among different classes based on the spectral reflectance values alone.
